Could Commonsense Reasoning improve CMC?
Commonsense Reasoning (CR) is based on the set of background information or world knowledge that an individual is intended to know or assume, and may be missing from context. On the other hand, Pragmatic reasoning, which the current CMC models cater to, is based on the derivation of explicit and implicit meanings within the context. The current MRC systems are nearing human performance on most datasets, however, they still perform poorly on single-turn CR based questions [1]}. While there is recently increasing interest in CR in the single-turn MRC setting [2]}, [3]}, [4]}, CMC remains relatively untouched. This may probably be due to the lack of foreknowledge requiring unanswerable questions (e.g. in SQuAD 2.0 [5]}) in current CMC datasets [6]}, suggesting a need for more complex CMC datasets that incorporate CR. However, humans annotators may often apply common-sense reasoning involuntarily while answering questions or comprehending, thus leaving room for incorporating CR in models. There seems to be no recent work that invalidates, experimentally, the role of CR in CMC. QuAC, for example, is drawn from articles on personalities, and current models still lag behind the human benchmark. It may be worth experimenting if adding domain knowledge or attributes about the context, like location and gender, help improve answering these questions.
